Although our focus is discriminative approach, we also compare with generative approaches for object placement assessment, because both types of approaches can be used to create reasonable composite images.
For discriminative models SOPA and FOPA, we obtain the positive composite image according to the highest pixel-wise rationality score in the score map, as described in Section .
For generative models TERSE [1]} and PlaceNet [2]}, we generate a reasonable placement and obtain the positive composite image. Note that FOPA and SOPA produce a rationality score map for a pair of scaled foreground and background. For fair comparison, when employing generative models TERSE and PlaceNet, we fix the foreground size and only predict a reasonable location for the foreground.
We show the positive composite images generated by SOPA, FOPA, TERSE, and PlaceNet in Figure REF . We can see that the SOPA model and our FOPA model can generate more realistic composite images than TERSE and PlaceNet, while our FOPA runs \(19000\times \)  faster than SOPA.
